非线性间歇过程PD型变增益迭代学习控制  

PD-type Variable Gain Iterative Learning Control For Nonlinear Batch Processes

作　　者：张秀赟 陶洪峰 杨慧中 ZHANG Xiu-yun;TAO Hong-feng;YANG Hui-zhong(Key Laboratory of Advanced Process Control for Light Industry Ministry of Education,Jiangnan University,Wuxi 214122,China)

机构地区：[1]江南大学轻工过程先进控制教育部重点实验室,江苏无锡214122

出　　处：《控制工程》2021年第9期1754-1758,共5页Control Engineering of China

基　　金：国家自然科学基金资助项目(61773181,61203092);中央高校基本科研业务费专项资金资助项目(JUSRP51733B)。

摘　　要：针对迭代学习控制中固定学习增益存在的系统收敛速度较慢,实际应用中系统动态性能较差的缺点,研究了具有批次重复性的非线性间歇过程变增益PD型迭代学习控制方法。利用迭代学习过程中的期望轨迹、控制输入以及跟踪误差信息,设计变增益PD型控制器,给出了谱半径收敛条件,并利用算子理论对其进行了证明。对微藻发酵过程基质浓度的控制进行建模仿真,用该算法与传统固定增益PD型控制方法对比,仿真结果验证了该控制策略的可行性和有效性。Aiming at the problem of slow convergence rate of fixed learning gain in iterative learning control and poor dynamic performance in practical application, a variable gain PD-type iterative learning control algorithm for batch repetitive nonlinear batch process is investigated. Using the desired trajectory, control input and tracking error information in the iterative learning process, a variable gain PD-type controller can be designed, and the convergence condition of spectral radius is given, which is proved by operator theory. The control of substrate concentration in microalgae fermentation process is modeled and simulated. Compared with the traditional fixed gain PD-type control algorithm, the simulation results validate the feasibility and effectiveness of the control strategy.

关 键 词：非线性 间歇过程 迭代学习控制 变增益 算子理论 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]